MLREL04-BP01 Automate endpoint changes through a pipeline
Manual change management can be error prone and potentially incur a high effort cost. Use automated pipelines (that integrate with a change management tracking system) to deploy changes to your model endpoints. Versioned pipeline inputs and artifacts allow you to track the changes and automatically rollback after a failed change.
Desired outcome: You establish a reliable and consistent deployment process for your machine learning models. You gain the ability to track changes, perform automatic rollbacks when needed, and improve adherence to change management policies. This approach reduces manual errors, increases operational efficiency, and provides you with better visibility into your ML deployment lifecycle.
Common anti-patterns:
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Making manual updates directly to production endpoints.
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Using different deployment processes across teams or environments.
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Lacking proper version control for model artifacts.
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Not having clear rollback mechanisms for failed deployments.
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Bypassing change management tracking systems.
Benefits of establishing this best practice:
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Reduces human error during deployments.
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Increases deployment speed and reliability.
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Improves traceability and auditability of changes.
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Enables rollback to previous versions.
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Improves adherence to change management policies.
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Supports scalable ML operations across multiple models.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Implementing automated pipelines for model endpoint changes brings consistency and reliability to your ML operations. By using a standardized deployment approach, you can verify that that changes to production endpoints follow the same validated process. This reduces the risk of deployment errors and improves overall operational efficiency.
The pipeline should include steps for testing, validation, and approval of model changes before they reach production. Integration with your existing change management system allows for proper tracking and documentation of changes. The pipeline should also maintain versioned artifacts of models deployed, enabling rollback if issues arise in production.
Implementation steps
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Set up a CI/CD pipeline for ML workloads. Establish a continuous integration and continuous delivery pipeline specifically designed for machine learning workloads. Amazon SageMaker AI Pipelines
provides a purpose-built solution for creating end-to-end ML workflows at scale. -
Integrate with change management systems. Connect your pipeline to existing change management tracking systems to document endpoint changes. This improves adherence with your organization's governance requirements and provides a full audit trail of deployment activity.
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Implement artifact versioning. Store model artifacts, code, and configuration files in version control. Use Amazon SageMaker AI Model Registry to catalog and version your models, making it straightforward to track which model versions are deployed to which endpoints.
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Define automated testing and validation. Include automated testing steps in your pipeline to validate model performance before deployment. This should include unit tests, integration tests, and model quality evaluations using metrics specific to your use case.
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Establish approval gates. Configure approval checkpoints in your pipeline where stakeholders can review changes before they are promoted to production. These gates maintain quality control and improve adherence to business requirements.
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Implement automated rollback mechanisms. Create automated procedures to roll back to the previous stable version if a deployment fails or if issues are detected after deployment. This minimizes downtime and impact on users.
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Monitor deployment metrics. Track the success rate of deployments and time-to-deployment metrics to continuously improve your pipeline. Use Amazon CloudWatch
to monitor these operational metrics.
Resources
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